class_docstr: Character-level CNN encoder to generate word representations, as input to
  RNN encoder.
docstr: This allows AdversarialTrainer to turn on retrain_grad when
  running adversarial example generation model.
docstr_tok: ["This","allows","AdversarialTrainer","to","turn","on","retrain_grad","when","running","adversarial","example","generation","model","."]
code: def set_gradient_tracking_mode(self, mode=True):
      """This allows AdversarialTrainer to turn on retrain_grad when
      running adversarial example generation model."""
      self.tracker.reset()
      self.track_gradients = mode
  
imports: import logging
  from ast import literal_eval
  import torch
  import torch.nn.functional
  from fairseq.models import FairseqEncoder, register_model, register_model_architecture
  from pytorch_translate import char_encoder, model_constants, rnn, utils, vocab_constants
  from pytorch_translate.common_layers import Embedding, VariableTracker
  from pytorch_translate.data.dictionary import TAGS
label_desc: Use image node_id 2 for calling the CharCNNEncoder obj's underlying member method code with example usage: obj.set_gradient_tracking_mode(mode) without return types